Deepseek-R1: DESTROYS O1 & Sonnet 3.5 – The True Open-Source Coding King Is Here!

Codewello
22 Jan 202516:18

TLDRThe Deepseek-R1, an open-source AI model, is tested against the Sonnet 3.5 for coding assistance. The R1 shows impressive performance, ranking just behind the O1 model in global benchmarks and even outperforming the Sonnet 3.5 in coding tasks. It offers significant cost savings, with pricing at 55 cents for 1 million input tokens and $2 for output, compared to the Sonnet 3.5's $22. However, the R1's slower response time and occasional errors in handling larger tasks are noted. Despite these drawbacks, the R1's logic and analysis capabilities are praised, making it a strong contender for those willing to trade speed for cost and functionality.

Takeaways

  • 😀 DeepSeek R1 is a fully open-source AI model that claims to rival O1 and compete with Sonnet 3.5.
  • 😀 The R1 can be accessed for free through Chad Deep or Open Router, with more options expected soon.
  • 😀 Benchmarks show R1 performs just behind O1 but above other models, with Sonnet 3.5 slightly ahead in coding.
  • 😀 R1's pricing is significantly cheaper than Sonnet 3.5, making it a cost-effective alternative.
  • 😀 R1 can be run locally on various hardware, with models ranging from 7B to 67B parameters.
  • 😀 In a coding test, R1 created a responsive terms and conditions page with some initial errors but quickly corrected them.
  • 😀 R1 improved a basic contact us page, adding design elements and functionality, though the design could be more creative.
  • 😀 R1 provided feedback on a server folder, suggesting improvements like adding Swagger documentation and security hardening.
  • 😀 The model's latency is a drawback, with response times sometimes reaching up to half a minute.
  • 😀 Despite its speed issues, R1 offers good logic and analysis capabilities, making it a strong contender in the open-source AI space.

Q & A

  • What is the DeepSeek R1 model?

    -The DeepSeek R1 is a fully open-source AI model that claims to rival the power of the O1 model and compete with the Sonnet 3.5 encoding capability.

  • How can one access and use the DeepSeek R1 model for free?

    -There are multiple ways to access and use the DeepSeek R1 model for free. One can go to the Chad Deep website, create an account, and use the DeepSeek provider, which is cheap and easy to use. Another option is using Open Router, which also offers the DeepSeek R1 model.

  • How does the DeepSeek R1 model compare to the Sonnet 3.5 in terms of coding performance?

    -According to the live benchmark, the DeepSeek R1 is just behind the O1 model and above the O1 Braev model and the Gemini experiment models. The Sonnet 3.5 is still considered the state-of-the-art coding model, but the DeepSeek R1 is very close, being only one point behind.

  • What are some of the tasks the DeepSeek R1 model was tested on in the video?

    -The DeepSeek R1 model was tested on tasks such as creating a terms and conditions UI page, improving the design of a contact us page, and providing feedback on a server folder structure.

  • How does the DeepSeek R1 model handle errors and corrections during coding tasks?

    -The DeepSeek R1 model can handle errors and corrections effectively. For example, it can import the wrong header but can be corrected by specifying the correct header. It can also add new translations to the page and handle seam switching and translation switching logic.

  • What are the pricing differences between the DeepSeek R1 and the Sonnet 3.5 models?

    -The DeepSeek R1 model is significantly cheaper than the Sonnet 3.5 model. For 1 million input tokens, the DeepSeek R1 costs 55 cents, while the Sonnet 3.5 costs $3. For 1 million output tokens, the DeepSeek R1 costs $2, while the Sonnet 3.5 costs $15. Additionally, the DeepSeek R1 has lower caching costs.

  • Can the DeepSeek R1 model be run locally, and if so, how?

    -Yes, the DeepSeek R1 model can be run locally. It is available in different sizes such as 7B, 8B, 32B, and 70B models, which can be downloaded from platforms like AMA. The 32B model is similar to the O1 mini, and the 70B model is the largest one available.

  • What are some of the limitations or issues encountered while using the DeepSeek R1 model?

    -One of the main limitations of the DeepSeek R1 model is its slower response time compared to the Sonnet 3.5. The latency for the provider to send the token is around 5-6 seconds, which can be a significant delay for some users.

  • What feedback did the DeepSeek R1 model provide on the server folder structure?

    -The DeepSeek R1 model suggested improvements such as adopting Zod for validation, centralizing error handling using RBAC middleware, adding Swagger documentation, and improving security hardening and testing structure.

  • What is the overall opinion of the DeepSeek R1 model based on the video?

    -The overall opinion of the DeepSeek R1 model is positive. It is praised for its logical capabilities and being an open-source alternative to the O1 model. However, the slower response time is a drawback that prevents a complete switch from the Sonnet 3.5 model.

Outlines

00:00

🧠 Introduction to DeepS R1 and Benchmark Testing

The script begins by discussing the release of DeepS R1, an open-source model from DeepS AI, which claims to rival powerful models like OpenAI’s GPT-4 and Sonet 3.5. The author tests R1 as a coding assistant, comparing its performance to Sonet 3.5 in terms of functionality, ease of use, and accuracy. They walk through setting up the model via DeepS API and OpenRouter, mentioning potential alternatives that may be available in the near future. Benchmark comparisons show DeepS R1 performing just behind OpenAI’s models and slightly ahead of other models like Gemini. The section also introduces R1’s versatility in coding tasks and prepares the audience for a deeper dive into the model's real-world applications.

05:01

🔧 Testing DeepS R1 in Practical Coding Tasks

In this section, the author starts testing DeepS R1 with a straightforward task: creating a responsive terms and conditions page with theme switching functionality. Despite a few minor errors like wrong header imports and language translation issues, R1 performs fairly well in correcting these errors. The model demonstrates quick adaptability, including handling translations between English and Arabic. The author commends R1 for making improvements quickly and efficiently, even though some issues arise along the way. Overall, the performance is promising and reveals the model’s strengths and weaknesses in real-world tasks.

10:03

💡 DeepS R1 vs. Sonet 3.5: Performance and Pricing

The author compares the cost-effectiveness of DeepS R1 with that of Sonet 3.5 in terms of API usage. While Sonet’s pricing for 1 million input/output tokens is higher, DeepS R1 offers significantly cheaper rates—just $0.55 per million input and $2 per million output. For those who can run R1 locally, it’s possible to use models like 7B, 8B, and even the larger 32B and 67B versions. The script also mentions DeepS R1’s impressive pricing model, especially for those seeking open-source solutions, but notes that local setup can be complex due to hardware requirements for larger models.

15:04

🎨 Testing DeepS R1’s UI Capabilities

The author proceeds to test R1’s UI capabilities by improving a basic ‘Contact Us’ page, adding headers, footers, and translation logic. Despite facing an error when the task was too large, splitting the task into smaller steps revealed R1’s strengths in UI layout and responsiveness, including theme switching functionality. The results, though functional, are less creative and modern than the author’s usual design preferences with Sonet 3.5. The slow response times during testing, with some delays up to 30 seconds, are noted as a limitation, especially when comparing R1’s speed with other models like Sonet 3.5.

⚙️ Analysis and Code Feedback with DeepS R1

In this paragraph, the author tests R1’s ability to analyze server-side code, focusing on an MVC pattern using Express.js. R1 provides useful feedback, including recommendations to adopt Zod for validation, improve error handling, and add Swagger documentation for API endpoints. The model also suggests security hardening and improvements in testing structure. While the author finds R1’s feedback helpful, they also note that the suggestions are fairly standard and didn't point out any major flaws in the existing codebase. The response is considered solid, with a few useful tips on improving the project.

💸 DeepS R1: A Cheaper Open-Source Alternative to Expensive Models

This section reflects on the broader implications of DeepS R1’s release. The author praises the open-source model for offering powerful capabilities at a fraction of the cost compared to proprietary models like OpenAI’s GPT-4. While DeepS R1 does not yet match Sonet 3.5 in terms of speed, it is much more affordable, with lower token input/output costs. The author emphasizes that while R1’s response times need improvement, the overall functionality and low-cost nature of the model make it a compelling option for developers seeking open-source solutions, especially given the increasing competition between closed-source and open-source models.

🚀 Conclusion: R1's Potential and Future Use

The conclusion reflects on the potential of DeepS R1 in the coding and AI assistant landscape. The author acknowledges R1’s impressive logic and functionality but mentions that the model’s slow response times prevent them from fully switching to it over Sonet 3.5. Despite this, the author expresses confidence that DeepS R1 will continue to improve and that they plan to test it further for backend development. The section closes by encouraging viewers to subscribe and thanking them for watching the video, as the author continues to explore DeepS R1’s capabilities in future content.

Mindmap

Keywords

💡Deepseek-R1

Deepseek-R1 is a fully open-source AI model introduced as a rival to the O1 model and the Sonnet 3.5. It claims to offer comparable power and encoding capabilities. In the video, the presenter tests Deepseek-R1 as a coding assistant, comparing its performance and functionality to other models like the Sonnet 3.5. The presenter evaluates its ability to handle various coding tasks and benchmarks its performance against other models.

💡Open Source

Open Source refers to software or models whose source code is made freely available and can be modified and redistributed by anyone. The video highlights that Deepseek-R1 is an open-source model, emphasizing its accessibility and potential for customization. This is in contrast to closed-source models like the O1, which are proprietary and not openly available for modification.

💡Coding Assistant

A Coding Assistant is an AI tool designed to help developers write code more efficiently by providing suggestions, completing code snippets, and identifying errors. In the context of the video, Deepseek-R1 is tested as a coding assistant in the Rookline environment to see how well it performs tasks such as creating responsive UI components and handling translations.

💡Benchmark

Benchmarking is the process of evaluating the performance of a system or model against a set of standard tests or metrics. The video mentions the Live Bench Benchmark, which is used to compare the performance of Deepseek-R1 with other models like the O1 and Sonnet 3.5. The presenter uses these benchmarks to assess the model's capabilities and determine its ranking among other coding models.

💡Responsive UI

Responsive UI refers to a user interface that adapts to different screen sizes and devices, ensuring a consistent and optimal user experience. In the video, the presenter tests Deepseek-R1's ability to create a responsive terms and conditions page that can switch between dark and light modes. This demonstrates the model's capability to handle UI design tasks and ensure compatibility across various devices.

💡Translation

Translation in the context of the video refers to the ability of the AI model to handle and generate content in multiple languages. The presenter tests Deepseek-R1's ability to add translations to a page, specifically mentioning the need to add translations to Arabic and English files. This highlights the model's multilingual capabilities and its ability to handle translation tasks efficiently.

💡Pricing

Pricing refers to the cost associated with using an AI model or service. The video compares the pricing of Deepseek-R1 with other models like the Sonnet 3.5, highlighting the cost-effectiveness of Deepseek-R1. The presenter mentions the significant difference in pricing, with Deepseek-R1 being much cheaper, making it an attractive alternative for developers.

💡Latency

Latency is the delay between a request being made and a response being received. In the video, the presenter notes the latency issues with Deepseek-R1, mentioning that it sometimes takes up to half a minute to get a response. This highlights a potential drawback of the model, as faster response times are generally preferred in coding environments.

💡MVC

MVC stands for Model-View-Controller, a software architectural pattern that separates an application into three interconnected components. The video mentions the use of MVC in the context of server folder organization and how Deepseek-R1 provides feedback on improving the structure. The presenter evaluates the model's ability to analyze and suggest improvements to the MVC architecture used in the project.

💡Swagger

Swagger is a tool used for documenting APIs, providing a way to visualize and interact with API endpoints. In the video, Deepseek-R1 suggests adding Swagger documentation to the project, highlighting the importance of proper API documentation for better maintainability and usability. The presenter considers this feedback as valuable for improving the project's documentation.

Highlights

Deepseek R1 is a fully open-source model claiming to rival O1 and compete with Sonnet 3.5.

Multiple ways to use the model for free, including Deepseek's own provider and Open Router.

Deepseek R1's benchmark results are impressive, with a global average just behind O1 and above O1 Braev.

In a coding task, Deepseek R1 created a responsive terms and conditions page with theme switching.

Deepseek R1 encountered errors but was able to correct them and add translations quickly.

Deepseek R1 is significantly cheaper than Sonnet 3.5, with lower input and output token costs.

Deepseek R1 can be run locally, with multiple model sizes available, including 7B, 8B, and 32B.

In a UI design task, Deepseek R1 improved a contact us page with header, footer, and seam switching.

Deepseek R1's UI design capabilities are functional but less creative than Sonnet 3.5.

Deepseek R1's latency is higher than expected, with an average response time of 5-6 seconds.

Deepseek R1 uses the code and prompts provided to train their models, which may be a concern for some users.

Deepseek R1 provided feedback on improving the server folder, suggesting the addition of Swagger and security hardening.

Deepseek R1 is an impressive open-source model, catching up to closed-source models in a short time.

Despite its slower response time, Deepseek R1 is a good alternative to Sonnet 3.5 for coding tasks.

The presenter plans to test Deepseek R1 in the backend in a future video.